The uncertainty of DNN-based odometry estimation has been explored by [1]}, [2]}.
They adopted a common strategy to convert the target predictions into a Gaussian distribution, conditioned on the mean value of pose estimates and its covariance. The parameters inside the framework are optimized via the loss function with a combination of mean and covariance. By minimizing the error function to find the best combination, the uncertainty is automatically learned in an unsupervised fashion.
In this way, the uncertainty of motion transformation is recovered.
The motion uncertainty plays a vital role in probabilistic sensor fusion or the back-end optimization of SLAM systems. To validate the effectiveness of uncertainty estimation in SLAM systems,
[1]} integrated the learned uncertainty into a graph-SLAM as the covariances of odometry edges. Based on these covariances a global optimization is then performed to reduce system drifts. It also confirms that uncertainty estimation improves the performance of SLAM systems over the baseline with a fixed predefined value of covariance.
Similar Bayesian models are applied to the global relocalization problem. As illustrated in [4]}, [5]}, the uncertainty from deep models are able to reflect the global location errors, in which the unreliable pose estimates are avoided with this belief metric.
